class_docstr: Construct a "fast" Blenderbot tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2
  tokenizer, using byte-level Byte-Pair-Encoding.
  
  This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
  be encoded differently whether it is at the beginning of the sentence (without space) or not:
  
  ```python
  >>> from transformers import BlenderbotTokenizerFast
  
  >>> tokenizer = BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B")
  >>> tokenizer("Hello world")["input_ids"]
  [6950, 1085, 2]
  
  >>> tokenizer(" Hello world")["input_ids"]
  [6950, 1085, 2]
  ```
  
  You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
  call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
  
  <Tip>
  
  When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
  
  </Tip>
  
  This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
  refer to this superclass for more information regarding those methods.
  
  Args:
      vocab_file (`str`):
          Path to the vocabulary file.
      merges_file (`str`):
          Path to the merges file.
      errors (`str`, *optional*, defaults to `"replace"`):
          Paradigm to follow when decoding bytes to UTF-8. See
          [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
      bos_token (`str`, *optional*, defaults to `"<s>"`):
          The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
  
          <Tip>
  
          When building a sequence using special tokens, this is not the token that is used for the beginning of
          sequence. The token used is the `cls_token`.
  
          </Tip>
  
      eos_token (`str`, *optional*, defaults to `"</s>"`):
          The end of sequence token.
  
          <Tip>
  
          When building a sequence using special tokens, this is not the token that is used for the end of sequence.
          The token used is the `sep_token`.
  
          </Tip>
  
      sep_token (`str`, *optional*, defaults to `"</s>"`):
          The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
          sequence classification or for a text and a question for question answering. It is also used as the last
          token of a sequence built with special tokens.
      cls_token (`str`, *optional*, defaults to `"<s>"`):
          The classifier token which is used when doing sequence classification (classification of the whole sequence
          instead of per-token classification). It is the first token of the sequence when built with special tokens.
      unk_token (`str`, *optional*, defaults to `"<unk>"`):
          The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
          token instead.
      pad_token (`str`, *optional*, defaults to `"<pad>"`):
          The token used for padding, for example when batching sequences of different lengths.
      mask_token (`str`, *optional*, defaults to `"<mask>"`):
          The token used for masking values. This is the token used when training this model with masked language
          modeling. This is the token which the model will try to predict.
      add_prefix_space (`bool`, *optional*, defaults to `False`):
          Whether or not to add an initial space to the input. This allows to treat the leading word just as any
          other word. (Blenderbot tokenizer detect beginning of words by the preceding space).
      trim_offsets (`bool`, *optional*, defaults to `True`):
          Whether the post processing step should trim offsets to avoid including whitespaces.
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code: def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
      is_split_into_words = kwargs.get("is_split_into_words", False)
      assert self.add_prefix_space or not is_split_into_words, (
          f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          "to use it with pretokenized inputs."
      )
  
      return super()._batch_encode_plus(*args, **kwargs)
  
file_path: transformers/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py
imports: import json
  from typing import List, Optional, Tuple
  from tokenizers import pre_tokenizers, processors
  from ...tokenization_utils_base import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_blenderbot import BlenderbotTokenizer
label_desc: Use image node_id 4 for calling the BlenderbotTokenizerFast obj's underlying member method code with example usage: obj._batch_encode_plus() and returns: super